Cell Detection
46 papers with code • 4 benchmarks • 4 datasets
Cell Detection
Benchmarks
These leaderboards are used to track progress in Cell Detection
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PanNuke
Fluo-N2DH-GOWT1
Most implemented papers
Cell Tracking via Proposal Generation and Selection
Microscopy imaging plays a vital role in understanding many biological processes in development and disease.
SpotNet - Learned iterations for cell detection in image-based immunoassays
poldap/SpotNet
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Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task.
Signet Ring Cell Detection With a Semi-supervised Learning Framework
Our framework achieves accurate signet ring cell detection and can be readily applied in the clinical trails.
Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories
Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells.
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning
We propose a cell segmentation method for analyzing images of densely clustered cells.
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation
naivete5656/WSCTBFP
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ECCV 2020
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i. e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response.
Table Structure Recognition using Top-Down and Bottom-Up Cues
sachinraja13/TabStructNet
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ECCV 2020
We present an approach for table structure recognition that combines cell detection and interaction modules to localize the cells and predict their row and column associations with other detected cells.
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures
ChristophReich1996/Cell-DETR
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For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances.
CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images
AnnabelChen51/CellTrack-R-CNN
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Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine.
